Python Polars Streaming and Large Dataset Processing

When a Parquet file exceeds available RAM, the usual pandas workflow stops being viable. Polars offers a different path: its lazy API can execute queries in streaming mode, processing data in batches rather than loading the entire dataset into memory. This article covers python polars streaming and large dataset processing, focusing on when streaming helps, which operations support it, and how to use it correctly.

How the Streaming Engine Works

Polars has two execution paths. The eager API (pl.DataFrame) loads data and executes operations immediately. The lazy API (pl.LazyFrame) builds a query plan that Polars optimizes before execution. When you call .collect() on a lazy frame, Polars executes the optimized plan.

In default mode, .collect() materializes the full result in memory. In streaming mode, Polars processes the query in batches. Each batch flows through the operator pipeline independently, and only the final result (or a bounded set of intermediate state) is retained in memory.

The streaming engine is built on the same Arrow columnar format as the rest of Polars. Each batch is a set of Arrow record batches, and operators process them one at a time. This is what keeps peak memory usage low: the engine never needs to hold the entire input or intermediate results simultaneously.

Enabling Streaming Mode

Streaming is enabled at collection time. The exact API depends on your Polars version.

In Polars 1.x, pass engine="streaming" to collect:

import polars as pl

lazy_df = pl.scan_parquet("large_file.parquet")
result = lazy_df.filter(pl.col("amount") > 1000).collect(engine="streaming")

In older versions (0.20.x and earlier), the parameter was streaming=True:

result = lazy_df.filter(pl.col("amount") > 1000).collect(streaming=True)

If you are on a recent version, engine="streaming" is the current form. Check your installed version with pl.__version__ if you are unsure which syntax applies.

A key point: streaming only works through the lazy API. You cannot stream operations on an eager pl.DataFrame. If you start with pl.read_parquet, the data is already in memory, and streaming provides no benefit.

Writing Results Without Collecting

collect(engine="streaming") still materializes the final result. If the result itself is too large for memory, you need a different approach: sink_* methods.

Polars provides sink_parquet, sink_csv, and sink_ipc on LazyFrame. These write the streaming output directly to disk without ever building the full result in memory:

lazy_df = pl.scan_parquet("large_file.parquet")

(
    lazy_df
    .filter(pl.col("region") == "europe")
    .group_by("customer_id")
    .agg(pl.col("amount").sum())
    .sink_parquet("output.parquet")
)

The sink_* methods are the true streaming output path. They are especially useful when the final result is itself too large to hold in memory, or when you want to chain multiple processing steps across files without intermediate materialization.

Operations That Support Streaming

Not every Polars operation can run in streaming mode. When an operation does not support streaming, Polars falls back to in-memory execution for that part of the plan. This means the streaming benefit can be lost silently if your query contains a non-streaming operation.

The following operations generally stream well:

The critical distinction is between operations that are per-batch (like filter and elementwise select) and operations that require global state (like global sort or pivot). Per-batch operations stream naturally. Global operations either need to buffer data or build an in-memory structure.

For group_by, Polars uses a two-phase approach in streaming mode. Each batch is partially aggregated, and the partial results are combined at the end. This keeps memory usage proportional to the number of distinct groups, not the number of rows.

Practical Example: Aggregating a Large Parquet File

Consider a scenario where you have a multi-gigabyte Parquet file with sales records and you need to compute total revenue per customer. The file is too large to load into memory with pl.read_parquet.

import polars as pl

lazy_df = pl.scan_parquet("sales_2024.parquet")

result = (
    lazy_df
    .filter(pl.col("status") == "completed")
    .group_by("customer_id")
    .agg(
        pl.col("revenue").sum().alias("total_revenue"),
        pl.col("order_id").count().alias("order_count"),
    )
    .collect(engine="streaming")
)

The filter runs per batch, discarding incomplete orders before aggregation. The group-by aggregation maintains a hash table of partial sums per customer, updated as each batch flows through. The final result is a small DataFrame with one row per customer, which is safe to materialize.

If the result were also too large for memory, you would use sink_parquet instead:

(
    lazy_df
    .filter(pl.col("status") == "completed")
    .group_by("customer_id")
    .agg(pl.col("revenue").sum())
    .sink_parquet("customer_revenue.parquet")
)

This writes the aggregated result directly to disk, bypassing in-memory materialization entirely.

When Streaming Does Not Help

Streaming is not a universal solution. There are several situations where it provides little or no benefit.

The input already fits in memory. If your dataset is a few hundred megabytes and your machine has plenty of RAM, streaming adds overhead without reducing peak memory in a meaningful way. The batching loop has some cost, and the optimizer may produce a less efficient plan in streaming mode for small data.

The bottleneck is the operation itself, not memory. If your query performs a global sort, streaming cannot avoid the sort's inherent cost. The sort must see all data before producing output, so the streaming engine either buffers data or falls back to in-memory execution.

The result is the problem. If the output of your query is itself larger than memory, collect(engine="streaming") will still fail. You need sink_* methods to write the output incrementally.

Your query contains non-streaming operations. If a pivot or global sort appears in the plan, that portion runs in memory. The streaming engine processes what it can, but the non-streaming operation becomes the bottleneck.

Streaming vs. Manual Chunking

Before Polars had a streaming engine, the common approach for large files was manual chunking: read the file in chunks, process each chunk, and combine results. This still works, but it has drawbacks that streaming addresses.

Manual chunking requires you to manage chunk boundaries yourself, handle state that spans chunks (such as group-by aggregations), write merge logic for partial results, and ensure the chunk size is appropriate for your data and memory. Each of these is a source of bugs and maintenance overhead.

Polars streaming handles these concerns internally. The engine manages batch sizes, maintains partial aggregation state, and merges results correctly. Your code expresses the full query as a single lazy plan, and the engine decides how to execute it.

Manual chunking is still useful when you need fine control over memory usage or when you are working with a format that Polars cannot scan lazily. But for Parquet, CSV, and IPC files, the streaming engine is usually the simpler and more maintainable choice.

Monitoring Memory Usage in Production

When you deploy a streaming pipeline, you should verify that memory usage is actually bounded. A simple way is to run the pipeline with a memory profiler and observe peak RSS (resident set size).

The key metric to watch is peak memory, not total processing time. If peak memory stays well below the input file size, streaming is working. If peak memory approaches the input size, a non-streaming operation is likely materializing data.

You can also check which operations in your plan support streaming by inspecting the query plan:

lazy_df.explain(streaming=True)

This shows the optimized plan with streaming annotations. Operations marked as streaming will run in batch mode; operations without that annotation will run in memory. Reviewing this output before deploying a pipeline can save you from discovering a memory blow-up in production.

Choosing Between Streaming and Alternatives

Polars streaming is the right choice when your dataset is larger than available memory, your query consists mostly of streaming-compatible operations, you want to avoid the complexity of manual chunking, and you are already using Polars for the rest of your pipeline.

Other tools may be better in specific situations:

• Dask: Useful when you need distributed execution across multiple machines. Polars streaming is single-machine.
• DuckDB: A good alternative for SQL-style analytics on large files, with its own streaming and external-memory capabilities.
• Manual chunking: Still relevant for formats Polars cannot scan lazily, or when you need explicit control over batch boundaries.

The decision is not about which tool is better overall. It is about whether your workload fits within a single machine and whether Polars' streaming-compatible operations cover your query patterns. For most single-machine large-file analytics, Polars streaming is the most direct solution.